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Student Performance Prediction Using C4.5 Decision Tree and
CART Algorithm
Kamal Bunkar1, Prof. Sanjay Tanwani
2
1Ph.D Scholar , School of Computer Science and IT DAVV, Indore 452001, India.
2Professor and Head of Department of Computer Science and IT DAVV, Indore 452001, India.
Abstract
Data Mining provides important techniques for diverse fields like
education. Work in the educational sector is growing increasingly due to
the vast amount of data from students that can be used to discover useful
patterns in learning behavior relating to students. Student success in
university courses is of great concern to higher education where the
success can be influenced by many factors. This paper is an attempt to
apply the processes of data mining, particularly classification, to help
improve the quality of the higher education system by analyzing student
data that influence student performance in courses. The proposed
recommendation model consists of two major components first, the
student learning methodology analysis, and second is the performance or
achievement prediction. For student learning behavior analysis the paper
includes the results and algorithm selection; further, it contains the
model of the student performance prediction model. In this context, the
two popular data mining algorithms termed as C4.5 and CART decision
trees are applied. Those techniques accept the student performance or
achievement data for training and by using the student’s current
performance, the future performance is prognosticated. The comparison
of the performance of both algorithms is studied for prediction accuracy,
error rate, time, and memory usages. The results illustrate the C4.5
decision tree-based performance prediction divulges higher accuracy
and lesser memory and shortertime consumption. Therefore in the near
future, the C4.5 decision tree algorithm is adopted.
Parishodh Journal
Volume IX, Issue II, February/2020
ISSN NO:2347-6648
Page No:1702
Keywords: Data Mining, Educational Data mining, Clustering
Algorithm,Classification alogorithm, student learning pattern and
performance.
I. INTRODUCTION
Data mining can be used for application design and its improvements [1]. Various techniques
are implied on data to obtain results as per requirements. There are two kinds of algorithms
supervised and unsupervised learning [2]. These algorithms are employed for various kinds
of tasks as prediction, classification, clustering, association mining, and others. In this work,
the data mining techniques are used that works with the educational sector data known as
Educational Data Mining (EDM) [3]. The proposed work primarily focused on empowering
the student’s learning and performance, enhancing the teacher’s productivity, obtaining the
student learning patterns, and recommending the relevant study or course material. Thus the
work is divided into three modules. In the first module, the clustering algorithm is employed
for obtaining a group of weak, average, and efficient students. These groups of students will
be helping us in finding learning behavior or pattern. Additionally, that also works as
feedback to educators or teachers for optimizing their methodology and offering the most
compatible or suitable resources.
The second module proffers a predictive technique for students’ performance or achievement
prediction. The predicted data is used for understanding the future improvement and growth
in the performance of a student. Finally, a recommendation model is proposed that
amalgamates modules for interpretation of the student’s learning behavior and recommends
the most compatible learning material. The EDM systems are not only recommending just the
employment of data mining algorithms but it also offers to explore the patterns and hidden
information in underdone academic data [4]. In this context, the four goals of the proposed
work are established. The first one is to understand the learning methodology of the students
and to ameliorate the productivity of teachers and educators. Therefore an unsupervised
learning model for student learning behavior analysis is prepared. The obtained consequence
of the experiments is also reported in this paper. Further, the paper is intended to predict the
performance or achievements of students to make available resources and future growth and
improvements of students.
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Volume IX, Issue II, February/2020
ISSN NO:2347-6648
Page No:1703
In this context, the supervised learning-based student performance prediction model is
introduced in this paper. This model and previously introduced model used for designing a
recommender system that offers the course materials for the learning to the students. In this
paper first, the recently conducted experiments are highlighted, and based on the results
analysis clustering algorithm is selected. Further, a model using supervised learning
techniques is proposed for predicting student performance. The student performance and
learning behavior, both are used for designing the required recommendation model.
II. PROPOSED WORK
The student behavior analysis is also termed as student learning pattern analysis. It helps to
understand the learning ability of the students [5]. Using these patterns we approximate how
the different group of students is learning and which group of students is weak. In ML and
DM for grouping the clustering algorithms are used [6]. The figure 1 shows the data model for
student learning pattern analysis. A Student dataset university student’s dataset as
experimental dataset. To find the groups of students, who has the similar performances or
learning behavior (i.e. low, mid and high).
Figure 1.Student Behavior Analysis
The input dataset preprocessed in next step.The data preprocessing is a step of data mining
where the data is optimized. That is cleaning operation one dataset [7].That may produce
conflicts during the decision making or can influence the actual target values. To
preprocessing is described in [first paper reference].
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Volume IX, Issue II, February/2020
ISSN NO:2347-6648
Page No:1704
For computing the students learning behavior most of the authors is favoring to use of
clustering algorithms. Thus we implemented three clustering algorithms, namely k-means
clustering [8], fuzzy c-means clustering (FCM) [9] and a kernel based fuzzy c means (KFCM)
[10] algorithm. The modified FCM algorithm is usages the Gaussian kernel function[11]. The
system user selects one of the algorithms for performing the experiments. The preprocessed
data is used with the clustering algorithm and the groups of students are created according to
their pattern similarity. After training the selected centroids used for categorizing data
according to the distance or membership values.The test samples are prepared using the
random selection of data instances that contains 30% of instances. The test data clustered
according to selected centroids. According to the categorized data the performance of
algorithms was calculated.The algorithm of this process is also available in [reference self].
The student’s performance dataset is evaluated using three clustering algorithms (i.e. k-
means, FCMand KFCM). These clustering algorithms creating students groups according to
their performance in three main categories i.e. low, medium and high. These groups of
students are helpful for preparing the teaching strategy for different performer students [12].
That enhances teacher’s productivity as well as student performance [13]. Based on the
carried out experiments performance of these three algorithms are measured.
Table 1.Performance Comparison of Clustering
Exp. No K-Means FCM Improved FCM
Accuracy (%) 76.22 80.76 85.31
Time (MS) 163.42 283 347.85
Memory (KB) 15523 17835 18586
The mean accuracy ofclustering algorithms is reported in figure 2 and table 4. The number of
experiment is carried out and the basis of captured results mean accuracy is calculated.
According to the results the k-means clustering is producing less accurate results as compared
to FCM and KFCM.The memory usages of the clustering algorithms are reported in figure
2(a). According to the results the KFCM algorithm is consuming the higher memory. The
time consumption of all three algorithms for students learning pattern analysis is reported in
figure 4(c). The Y axis shows the time consumed in milliseconds (MS). Therefore according
to the performance of k-means is efficient as compared to FCM and improved FCM
algorithm. But in terms of accuracy the improved FCM is winner.
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Volume IX, Issue II, February/2020
ISSN NO:2347-6648
Page No:1705
Recently we proposed three modules to be implement, a model for student learning pattern or
behavior analysis. That technique usage the clustering algorithm the learning behavior is
identified. The student performance data is used and the models performance is evaluated.
The kernel based FCM is accurate enough. In this work the accuracy is the key parameter for
algorithm selection. Thus in further experiments the KFCM (Kernel based FCM) is being
used.
In this work two other data models of supervised learning algorithm namely CART
(classification and regression tree) [14] algorithm and C4.5 [15] is used. Using the efficient
classifier the student performance prediction system is proposed. That helpsto the user
(student/teachers) to get feedback about the student using performance prediction.
III. STUDENT PERFORMANCE PREDICTION
The performance of a student is an indicator of teacher’s efforts. Therefore in order to track
the performance of students some technique is required [16]. In literature there are a number
of ML techniques available for predicting student’s performance. In this work we prepared a
data model for analyzing the historical learning performance pattern and predict the next
performance based on the current values. Therefore a data mining model is presented. The
student performance prediction model is demonstrated in figure 3. The student performance
dataset collected previously using online sources for higher education course is used here.
That dataset contains different attributes to indicate the student’s performance. Additionally
there are two class labels are available. That dataset is used with the proposed model for
predicting the student performance.
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ISSN NO:2347-6648
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The aim of data preprocessing is to clean the noise and unwanted data. Therefore the
preprocessing techniques are normally utilized.In this presented work the dataset is used in a
vectored format thus the previously reported preprocessing algorithm is used for data
completeness null values are removed. According to the algorithm the dataset D contains rows
and columns in a vector. During the evaluation of attributes if any attribute of a data instance
is missing or null then the row is removed otherwise the data instance is included to a data
vector.The supervised data mining algorithms requires a set of pre-identified training samples.
These samples used to creates a data model and using which the similar patterns are identified.
The dataset is subdivided into two parts training and testing. The 70% of randomly selected
data were used for training of the classifiers. Remaining 30% of randomly selected data are
used with the trained data model as test data contains the class labels also for validation of the
predicted labels.
Figure 3.Student Performance Prediction Model
In this phase two decision tree algorithms namely CART and C4.5 algorithms are
implemented with the help of WEKA data mining tool and JAVA technology. User can select
an appropriate decision tree algorithm for conducting the experiments.The work includes the
predictive data modeling for student performance. Thebasic details about both the decision
tree algorithms are explained here. The system needs to generate the prediction for student
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ISSN NO:2347-6648
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performance therefore the C4.5 and CART algorithm is used. First we discuss the C4.5
decision tree. It is an extension of a decision tree ID3. That usage the concept of information
gain (IG) to create the data partitions. The attribute with the highest IG is selected to make the
decision. The C4.5 algorithm then using partitioned sub lists a complete decision tree is
developed. The algorithm considers the following basic constraints [14].
1. If samples in dataset contain same class then it simply creates a leaf node as decision tree.
2. If IG is not feasible then it creates a node higher up then tree using the expected value of
class.
3. If unseen class encountered, it creates a decision node using the target value.
To define IG, first require to discuss entropy. For instance the decision tree has two
categories, i.e. P (positive) and N (negative). Thus a set S, containing these positive and
negative targets, the entropy of S is:
𝐸𝑛𝑡𝑟𝑜𝑝𝑦 𝑆 = −𝑃 𝑃𝑜𝑠 𝑙𝑜𝑔2𝑃 𝑃𝑜𝑠 − 𝑃(𝑛𝑒𝑔)𝑙𝑜𝑔2𝑃(𝑛𝑒𝑔)
P (pos): proportion of positive examples in S
P (neg): proportion of negative examples in S
As already discussed, for cutting down the depth of a decision tree, while traversing the same,
selection of the best possible characteristic is mandatory in order to split the tree, this clearly
shows that attribute with minimum drop of entropy will be the best pick. Here, the IG can be
termed as required drop in entropy in relation with an attribute during the tree splitting. The
IG, Gain (E, A) of an attribute A can be defined as:
𝐺𝑎𝑖𝑛 𝐸, 𝐴 = 𝐸𝑛𝑡𝑟𝑜𝑝𝑦 𝑠 − 𝐸𝑣
𝐸𝑋𝐸𝑛𝑡𝑟𝑜𝑝𝑦 𝐸𝑣
𝑣
𝑛=1
The IGis used to decide positions of attributes and to construct trees in which every node is
positioned are attributes with maximum IG.Among those attributes that are not considered in
the path from the root yet. The intention is:
1. To generate small size tree and identify records after a handful steps.
2. To attain the desired level of unfussiness of the decisional approaches.
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ISSN NO:2347-6648
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CART Algorithm
CART [66] (Classification and Regression Trees) is introduced by Brieman, based on Hunt’s
algorithm. It handles both ( i. e. categorical and continuous) attributes to build a tree. It
handles missing values and uses Gini Index (GI) as selection measure. CART produces binary
splits. GImeasure uses cost complexity pruning to remove the unreliable branches from the
tree to improve the accuracy.To measure degree of impurity the GI is used.That is defined as:
𝐺𝑖𝑛𝑖 𝑇 = 1 − 𝑝𝑗2
𝑛
𝑗=1
GI of a table consist of single class is zero because the probability is 1 and 1 − 12 = 0.
Similar to Entropy, GI also reaches maximum value when all classes in the table have equal
probability. To work out the information gain for A relative to S, first it needs to calculate the
GI of S. Here S is a set of 120 examples are 70 “First”, 19 “Second”, 15 “Third” and 16
“Fail”.
𝐺𝑖𝑛𝑖 𝑆 = 1 − 𝑃𝑓𝑖𝑟𝑠𝑡 log2 𝑃𝑓𝑖𝑟𝑠𝑡 − 𝑃𝑠𝑒𝑐𝑜𝑛𝑑 log2 𝑃𝑠𝑒𝑐𝑜𝑛𝑑 − 𝑃𝑡𝑖𝑟𝑑 log2 𝑃𝑡𝑖𝑟𝑑
To determine the best attribute for a particular node IG is calculated. The information gain is
defined as,
𝑃𝑓𝑎𝑖𝑙 log2 𝑃𝑓𝑎𝑖𝑙 = 1 − 0.582 + 0.152 + 0.1252 + 0.1332
𝐺𝑖𝑛𝑖 𝑆 = 0.6015
So,
𝐼𝐺 𝑓 = 𝑓𝑖(1 − 𝑓𝑖)
𝑚
𝑖=1
GI and IGare calculated for all the nodes. As the result of the calculation, the attribute ParQua
is used to expand the tree. Then delete the attribute of the samples in these sub-nodes and
compute the GI and the IG to expand the tree using the attribute with highest gain. Repeat the
process until the Entropy of the node equals null. At that moment, the node cannot be
expanded anymore because the samples in this node belong to the same class.
Parishodh Journal
Volume IX, Issue II, February/2020
ISSN NO:2347-6648
Page No:1709
Decision tree: the decision tree algorithm works on the input training samples and produces a
tree. In this tree the branches of tree includes the combination of dataset attributes with their
values. The nodes contain the attribute name and edge contains the values. Finally in the leaf
node the decisions are available which is predicted when the testing data instances are applied.
Prediction outcomes: the testing data with the class labels are used with the previous phase
prepared tree. Using the available attributes and it’s values the decision tree is traversed and
the prediction using the leaf node is performed. The predicted outcome and dataset outcome is
compared to get the prediction accuracy.
This model help to predict the performance of students, the next section provide the results
analysis of these two algorithms.
IV. RESULTS ANALYSIS
Rightness of any calculation is estimated by its precision. Through precision of any
calculation we find that how great a calculation is. The following is the recipe to ascertain
exactness.
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 % =𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑆𝑎𝑚𝑝𝑙𝑒𝑠
𝑡𝑜𝑡𝑎𝑙 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 𝑡𝑜 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑦𝑋100
Table 2.Accuracy Comparison
Experiment No C4.5 CART
1 84.32 84.89
2 85.86 84.32
3 86.23 86.74
4 88.54 87.36
5 90.23 89.36
6 93.56 92.15
7 95.36 93.63
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Volume IX, Issue II, February/2020
ISSN NO:2347-6648
Page No:1710
Figure 4.Accuracy (%)
The accuracy of both the decision tree algorithms is demonstrated in figure 4 and table 2. The
line graph contains accuracy in Y axis. That is measured here in percentage (%). The X axis
shows the different experiments performed. According to the results the accuracy of
algorithm is varying but not 95%. Additionally the blue line (C4.5) shows the clear winner in
terms of accuracy of classification. Similarly error rate is the indication of misclassifications
of samples. It is measured on the bases of misclassified of instances and total instance to
classify.
𝐸𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒 % =𝑡𝑜𝑡𝑎𝑙 𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑙𝑦 𝑢𝑛𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑆𝑎𝑚𝑝𝑙𝑒𝑠
𝑡𝑜𝑡𝑎𝑙 𝑠𝑎𝑚𝑝𝑙𝑒𝑠 𝑡𝑜 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑦𝑋100
or
𝐸𝑟𝑟𝑜𝑟 𝑟𝑎𝑡𝑒(%) = 100 – Accuracy
Table 3.Error Rate
Experiment No C4.5 CART
1 15.68 15.11
2 14.14 15.68
3 13.77 13.26
4 11.46 12.64
5 9.77 10.64
6 6.44 7.85
7 4.64 6.37
7880828486889092949698
1 2 3 4 5 6 7
Acc
ura
cy (
%)
Experiments
C4.5 CART
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ISSN NO:2347-6648
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Figure 5.Error Rate
The error rate is unsuccessfulness of a classifier, thus less error rate means goodness of
classifier. Figure 5 and table 3 shows the error rate of both algorithms where X axis includes
the observations collected in table 3 and their line graph is given in figure 5. Here Y axis
includes the error rate (%). According to the results the C4.5 repots less error as compared to
CART in our Dataset. In comparison C4.5 consumes lesser memory then CART, because
C4.5 reduces all the ambiguity from tree by pruning hence the tree size in smaller than
CART. This observation is demonstrated in figure 6 and table 4. In most of the experiments
the blue line shows low consumption as compared to CART. Thus we select the C4.5 for
further experiments.
Table 4.Memory Comparison
Experiment No C4.5 CART
1 13000 13628
2 13882 13931
3 14293 13829
4 13628 13909
5 13843 14294
6 13727 15391
7 14048 14726
0
2
4
6
8
10
12
14
16
18
1 2 3 4 5 6 7
Err
or
Ra
te (
%)
Experiments
C4.5 CART
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Figure 6.Memory Usage
The time utilization of algorithms is also an essential fact of algorithm selection. In this
context the time consumption of algorithms for increasing amount of data is measured. The
table 5 and figure 7 reports the time consumption of both the algorithms. According to the
results the C4.5 algorithm requires less amount of time as compared to the CART algorithm.
Therefore the C4.5 algorithm is selected for further experiments.
Table 5.Time Consumption
Experiment No C4.5 CART
1 30 90
2 80 150
3 120 220
4 135 300
5 150 350
6 170 400
7 200 450
11500
12000
12500
13000
13500
14000
14500
15000
15500
16000
1 2 3 4 5 6 7
Me
mo
ry (
KB
)
Experiments
C4.5 CART
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Figure 7.Time Consumption
V. CONCLUSION & FUTURE WORK
The primary point of the proposed insightful work is to distinguish a powerful methodology
which serves to the understudies for getting criticism and suggesting the reasonable course
material. In this setting the proposed work is roused to plan an exact and successful
suggestion model that comprehend the understudy learning conduct, their status and
accessible course structure and it multifaceted nature. Utilizing every one of these variables
the total proposal framework is attempted to structure. Be that as it may, we have to parts first
understudy learning conduct and their exhibition expectation. The principal parts results are
accounted for here first and afterward the understudy execution forecast model is presented in
this paper.Currently the student performance prediction model usages the C4.5 and CART
algorithm for prediction but based on the experimental analysis as given in table 6.
Table 6.Mean Performance of Classifiers
S. No. Parameters C4.5 CART
1 Accuracy 89.15 88.35
2 Error Rate 10.84 11.65
3 Memory Usage 13774.42 14244
4 Time consumption 126.42 280
The experimental results given in table 6 that provides the mean performance of both the
classifiers. According to the obtained results the performance of C4.5 algorithm found
0
50
100
150
200
250
300
350
400
450
500
1 2 3 4 5 6 7
Tim
e (
MS
)
Experiments
C4.5 CART
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acceptable for the experimental dataset. In near future the proposed work is extended for
implementing the course material recommendation system design.
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